Robustly Recognizing Irregular Scene Text by Rectifying Principle Irregularities
Reading irregular scene text is a challenging problem in scene text recognition. Rectification is a popular measure to reduce irregularities of text in images. Existing rectification methods seek to rectify text images into a strictly regular form via free parametric transformation functions. However, they always suffer from information loss or severe deformation due to their poor constraints to the transformation functions. In our investigation, we found that CNN and attention are robust to many slight irregularities. That inspires us to propose a novel and effective rectification method that mainly rectifies the principle regularities, and leaves the slight irregularities to the CNNLSTM-attention recognizer. Our rectification method first estimates the character densities and directions of the input image in a down-sampled map, then finds a best fitting curve from a small predefined Bezier curve set, and finally rectifies the input image with a transformation function corresponding to the selected curve. Transformation functions are carefully designed so that they neither lose important visual information nor cause severe deformation. Extensive experiments on seven benchmark datasets show that our method achieves the state of the art performance in most cases, especially in curved text recognition.